To add these channels you have to extract the parameters from known data. This means extracting Boltzmann curves and time constant information so you can tell the channel which voltages activate it and inactivate it and how fast to open and close.

Activation (Boltzmann) curve for fast sodium channel

This step is tricky and can take a long time, but there is some software that can help. The Enguage Digitizer is one tool I could not live without.

Enguage is basically a tool that allows you to manually trace curves from published figures to get the curve data as an excel or .csv file. First you add axis points using the button at the top that has red plus signs on it. You tell the software what values each of the 3 corners of the graph are. Then you click the blue plus signs button and start to trace your graph, like so:

using Enguage digitizer to extract channel data

Then you export the data as whichever type of file you want. Pretty nice!
I like to have the data this way because then I can overlay this figure trace with any other trace I want and can manually fit an equation to it.

Channels are a hugely important part of a computational model. A recent paper from Eve Marder's lab shows that even with a very simple morphological model (just a soma), interesting electrical characteristics can be seen simply by manipulating the channels.

Kispersky et al., (2012) introduce an interesting paradox. They show that when you increase the sodium channel conductance you see more action potentials with low current injections (like 200pA). This is expected because the sodium channel is what causes the upswing of the action potential and more sodium is thought to mean more excitability. However, the authors find that when a high current injection is given (like 10nA), the increased sodium channel conductance actually decreases the firing rate. This is counter-intuitive because it goes against the more sodium=more excitability rule.

This is a pretty cool finding published in the Journal of Neuroscience using only a simple one-compartment model. The finding is based entirely on channel manipulation, and demonstrates how important these intrinsic channels are to any computational model.